A variety of increasingly sophisticated devices are now being used in the food processing industry for automatically sorting food products. Many of these devices perform visual or optical inspection of food products to identify individual food articles having specified undesirable visual characteristics. Modern, high-speed, optical-based sorting devices are capable of efficiently removing or diverting such food articles from a high-speed flow of food articles.
U.S. Pat. No. Re. 33,357, assigned to Key Technology, Inc., of Walla Walla, Wash., describes one example of a food processing device which detects and removes defective products based upon their optical characteristics. Key Technology manufactures and sells a variety of such optical-based sorting systems, including systems utilizing color inspection cameras. Sorting systems such as these use wide belts to convey a random lateral distribution of individual food articles past an inspection station. The inspection station identifies undesirable or defective articles and removes them from the product flow.
One persistent limitation of prior art sorting devices such as these is that their correct operation is significantly dependent upon operator setup and monitoring. For example, an operator must somehow instruct a sorting device as to the nature of "defective" food articles. This involves, at a minimum, specifying a range of camera intensity variations corresponding to product colors or shades considered to be undesirable. With a color sorting system there are many ranges from which to choose, potentially making this aspect of system setup somewhat complex. To simplify the process, some systems, such as those manufactured by Key Technology, are able to "learn" acceptable shade variations by inspecting a product sample having no defects. Such systems then assume that other shade variations are undesirable. Often, it is also desirable to set size thresholds corresponding to different types of defects. This requires additional instructions from an operator.
Despite the above "learning" features, fine-tuning a sorter almost always involves manual adjustment of a plurality of interacting parameters. Setting up an optical sorting system for correct operation thus requires an experienced and capable operator. Even assuming such an operator is available, however, optimum results are not always obtained. One reason for this is the many ambiguities present in setting a precise division between acceptable and defective products. These ambiguities often arise because of the variable nature of incoming product, because of data processing constraints, because of imperfections in obtaining the data upon which decisions are based, and because of the imprecise manner in which defective articles are separated from the product stream in many sorting devices. Because of these ambiguities, commercial automated sorters cannot be completely accurate in their identification of defective articles. Trade-offs and compromises are usually involved in determining optimum settings. For instance, sorter sensitivities can be increased to produce a corresponding increase in the number of defective products which are correctly identified and rejected. However, increasing sorter sensitivities often also increases the number of acceptable products which are erroneously identified as being defective. Most efficient operation is attained when an appropriate compromise is reached.
The problems noted above are not completely unique to automated sorters. In fact, many of the same problems are present when sorting is performed manually, by human inspectors. Because of the impossibility of obtaining a "perfect" sort, processing lines are intended to produce finished products within a range of targeted quality parameters or statistical objectives. Such parameters or objectives specify the nature of articles to be considered defective, and also specify a maximum permissible allowance of different types of defects within the overall finished product.
In automated systems, it is desirable to purposely exploit any available defective product allowances in order to minimize the number of acceptable products which are erroneously rejected as defective and to increase overall yield. Therefore, to achieve maximum efficiency an automated sorting device is set to a minimum sensitivity such that it will limit the presence of defective product within the finished product to just below the specified allowance. In other words, the optimal settings will reject no more product than is necessary to meet specified statistical objectives. This reduces the number of acceptable articles which are erroneously rejected, and increases the overall product yield.
Regardless of whether sorting is performed manually or by machine, periodic quality control inspections are required to ensure that the finished product meets specified quality objectives. In the past, these inspections have been conducted manually, by human quality control inspectors. Finished product quality inspection involves not only identifying defective and other types of products within a product sample, but also counting the relative number of such products. Numerous samples must typically be inspected to produce reliable quality statistics regarding the finished product.
Quality inspection and verification has more recently been performed by an automated quality analysis device, known as an AccuScan quality control monitor, available from Key Technology. This is a prior art device which utilizes a calibrated and stabilized color camera to produce statistical data regarding product quality. It allows an operator to specify defective product regions on a color image of an actual food article sample. The device then takes periodic "snapshots" of a food product stream and produces corresponding quality statistics, based upon the specifications made by the operator. These statistics are available on a generally continuous basis. Further information regarding the AccuScan quality control monitor is available from Key Technology and from U.S. Pat. No. 5,335,293 entitled Product Inspection Method and Apparatus, issued Aug. 2, 1994. This patent is incorporated herein by reference.
If quality statistics show that the finished product is out of tolerance, corrective measures must be taken. Such measures usually involve adjusting one or more sorter sensitivity settings or other sorting criteria settings. Skill and experience is required to predict which settings must be changed to improve results. One common mistake is to ignore the rejected products and to focus only on the finished product. This tends to result in the use of overly aggressive sorting criteria. While this ensures a high-quality finished product, it often reduces product yields by causing rejection of more product than necessary.
An optimal setup requires knowing not only the quality of the finished product, but also the quality of the rejected products. This is necessary to evaluate the number of acceptable products which have been erroneously rejected from the product stream. Proper setup of a sorting device requires keeping this number, which is not ascertainable from an inspection of the finished product alone, to a minimum. Accordingly, quality control procedures must involve both the accepted and the rejected products. In the past, this has required extensive human analysis or a pair of AccuScan quality control monitors.
On-going monitoring of sorting performance is also required. Sorter performance tends to vary with time, depending on the physical characteristics of the starting food products, on potentially drifting electrical or optical characteristics of the sorter, and on environmental or ambient conditions. Sorter settings must be updated periodically to maintain optimum performance. The operator skill and experience required at initial setup are thus required at many times during sorter operation. Providing optimal settings for automated sorting systems requires significant and on-going effort, despite the recent availability of automated quality monitoring monitors such as Key Technology's AccuScan.